Application of Masked RCNN for segmentation of brain haemorrhage from Computed Tomography Images

Automated analysis of CT scan images using AI solutions to diagnose abnormalities will help in overcoming the costly, time consuming and prone to error from manual analysis. Deep Learning has proved to be quite efficient to mimic human cognitive abilities (and even exceed that in many cases), especially with unstructured data.

DL algorithms can detect, localize and quantify a growing list of brain pathologies including intra-cerebral bleeds and their subtypes, infarcts, mass effect, midline shift, and cranial fractures. So, with advanced DL algorithms, analysis of radiographic data can be easily achieved and this can accelerate early detection of certain critical medical conditions, powered by AI.

As mentioned, Deep Learning algorithms for computer vision use cases has been extremely successful for classification and localization related problems. With the availability of annotated dataset, object of interest or region of interest segmentation using Deep Learning has been plausible.

Algorithms like Regional Convolutional Neural Network (RCNN) and it’s evolved forms, Faster RCNN and Masked RCNN is being widely used in the field of advanced radiology to auto detect medical conditions through radio-graphic images.

For this session, I am particularly going to talk about application of Masked RCNN for detection of regions of brain haemorrhage from CT scan images of the brain.


Outline/Structure of the Demonstration

Topics to be discussed

  • Need of an automated AI based solution in CT Scan Image Analysis (2 mins)
  • Importance of Deep Learning based solutions for localizing object of interests within CT Scan images (2 mins)
  • Efficient usage of Masked RCNN for segmentation of brain hemorrhage from CT Scan Images (8 mins)
  • Working demo and explanation of how Masked RCNN functions in segmenting brain hemorrhage (8 mins)

Learning Outcome

1. Basic intuition of Deep Learning to solve instance segmentation in computer vision domain

2. Wide range of applications for Mask RCNN for real time instance segmentation from images.

Target Audience

AI Researchers, ML Engineers, DL Engineers, Data Scientists

Prerequisites for Attendees

1. Working knowledge on Machine Learning

2. Working knowledge on Deep Learning

3. Basics of maths and statistics



schedule Submitted 3 years ago

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    20 Mins

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  • Aditya Bhattacharya

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